Prediction of Transient NOx Emissions from a Non-Road Heavy-Duty Diesel Engine Based on the PSO-XGBoost Algorithm

This paper proposes a machine learning-based framework for predicting transient nitrogen oxide (NOx) emissions from non-road heavy-duty diesel engines under NRTC conditions. The model combines the PSO and XGBoost algorithms to predict transient NOx emissions from non-road heavy-duty diesel engines. Experimental results were obtained through engine bench tests. Pearson’s correlation coefficient (PCC), Spearman’s correlation coefficient (SCC), and SHAP analysis were used to evaluate the relationship between engine operating parameters and NOx emissions, thereby improving the interpretability of features and the reliability of input parameter selection. The hyperparameters of the XGBoost model were optimized using the PSO algorithm. The optimized model exhibits good predictive performance. On the training set, the model’s R2 coefficient reached 0.9989, MAE was 1.16 ppm, and RMSE was 1.57 ppm. On the test set, the R2 value was 0.9662, and the MAE and RMSE were 6.33 ppm and 8.97 ppm, respectively. Furthermore, the PSO-XGBoost model was compared and analyzed with six traditional models. Finally, the generalization of the PSO-XGBoost model and its underlying mechanisms were discussed. The results indicate that the proposed PSO-XGBoost framework can effectively capture nonlinear relationships among diesel engine operating parameters and achieve accurate transient NOx prediction for non-road heavy-duty diesel engines.